Integration of Fractal and Multivariate Principal Component Models for Separating Pb-Zn Mineral Contaminated Areas | ||
| Journal of Mining and Environment | ||
| مقاله 20، دوره 14، شماره 3، مهر 2023، صفحه 1019-1035 اصل مقاله (5.46 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2023.13227.2424 | ||
| نویسندگان | ||
| Hossein Mahdiyanfar1؛ Mirmahdi Seyedrahimi-Niaraq* 2 | ||
| 1Department of Mining Engineering, University of Gonabad, Gonabad, Iran | ||
| 2Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| چکیده | ||
| The primary purpose of this investigation is contamination mapping in surrounding areas of Irankuh Pb–Zn mine, located in central Iran, using an integrated approach of principal component analysis (PCA) with the Concentration-Area (C-A) and Power Spectrum-Area (S-A) fractal models. PCA categorized the 45 elements into eight principal components. Component 2, containing the toxic elements of Pb, Zn, As, Mn, Cd, and Ba, was identified as the contamination factor. This multivariate contamination factor was modeled using the C-A and S-A fractal methods (in spatial and frequency domains) to delineate pollution areas. Modeling of PCA data using the C-A fractal method showed four main populations for the contamination factors. Two populations with higher fractal dimensions are associated with contamination from mining activities or anthropogenic effects. Low fractal dimensions are considered the background population, which has not been affected or is less affected by these activities. Five geo-chemical populations were obtained for contamination factors using the S-A fractal modeling of PCA in the frequency domain. Therefore, various geo-chemical populations were achieved using geo-chemical filtering and two-dimensional inverse Fourier transformation. The geo-chemical populations related to classes 2, 3, and 4 containing intermediate frequency signals showed the pollution anomaly. The spatial distribution of pollutant geo-chemical signals exhibits excellent conformity with the mining operation limit and tailing dam location as pollutant sources. The results indicate that the elements Pb, Zn, Cd, and As have significant values in the surrounding soils rather than their concentrations in the earth’s crust. The results demonstrate that the S-A fractal models can more precisely delineate the environmental anomaly than the C-A fractal model, especially in intermediate frequency populations. | ||
| کلیدواژهها | ||
| Fractal model؛ Principal Component Analysis؛ Environmental pollution؛ geochemical signals | ||
| مراجع | ||
|
[1]. Zhang, B., Jia, T., Peng, S., Yu, X., and She, D. (2022). Spatial distribution, source identification, and risk assessment of heavy metals in the cultivated soil of the Qinghai–Tibet Plateau region: Case study on Huzhu County. Global Ecology and Conservation, e02073.
[2]. Diatta, J.B., Chudzinska, E., and Wirth, S. (2008). Assessment of heavy metal contamination of soils impacted by a zinc smelter activity. Journal of Elementology. 13 (1).
[3]. Yousefi, M. and Hronsky, J.M. (2023). Translation of the function of hydrothermal mineralization-related focused fluid flux into a mappable exploration criterion for mineral exploration targeting. Applied Geochemistry, 105561.
[4]. Anaman, R., Peng, C., Jiang, Z., Liu, X., Zhou, Z., Guo, Z., and Xiao, X. (2022). Identifying sources and transport routes of heavy metals in soil with different land uses around a smelting site by GIS based PCA and PMF. Science of The Total Environment, 153759.
[5]. Huang, Y., Li, T., Wu, C., He, Z., Japenga, J., Deng, M., and Yang, X. (2015). An integrated approach to assess heavy metal source apportionment in peri-urban agricultural soils. Journal of Hazardous materials, 299, 540-549.
[6]. Seyedrahimi-Niaraq, M., Mahdiyanfar, H., and Mokhtari, A.R. (2022). Integrating principal component analysis and U-statistics for mapping polluted areas in mining districts. Journal of Geochemical Exploration, 234, p.106924.
[7]. Cheng, Q., Agterberg, F.P., and Ballantyne, S.B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51, 109–130.
[8]. Zuo, R. (2014). Identification of geochemical anomalies associated with mineralization in the Fanshan district, Fujian, China. Journal of Geochemical Exploration, 139, 170-176.
[9]. Rahimi, H., Abedi, M., Yousefi, M., Bahroudi, A., and Elyasi, G.R. (2021). Supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof. Applied Geochemistry, 128, 104940.
[10]. Afzal, P., Farhadi, S., Boveiri Konari, M., Shamseddin Meigooni, M., and Daneshvar Saein, L. (2022). Geochemical anomaly detection in the Irankuh District using Hybrid Machine learning technique and fractal modeling. Geopersia.
[11]. Daneshvar Saein, L., Afzal, P., Shahbazi, S., and Sadeghi, B. (2022). Application of an improved zonality index model integrated with multivariate fractal analysis: epithermal gold deposits. Geopersia. 12 (2): 379-394.
[12]. Paravarzar, S., Mokhtari, Z., Afzal, P., and Aliyari, F. (2023). Application of an approximate geostatistical simulation algorithm to delineate the gold mineralized zones characterized by fractal methodology. Journal of African Earth Sciences, 200, 104865.
[13]. Sinclair, A.J. (1991). A fundamental approach to threshold estimation in exploration geochemistry: probability plots revisited. Journal of Geochemical Exploration. 41(1-2): pp.1-22.
[14]. Moradpouri, F. and Ghavami-Riabi, R. (2020). A multivariate geochemical investigation of borehole samples for gold deposits exploration. Geochemistry International, 58, 40-48.
[15]. Seyedrahimi-Niaraq, M. and Hekmatnejad, A. (2021). The efficiency and accuracy of probability diagram, spatial statistic and fractal methods in the identification of shear zone gold mineralization: a case study of the Saqqez gold ore district, NW Iran. Acta Geochimica.
[16]. Xiao F, Wang K, Hou W, and Erten O. (2020). Identifying geochemical anomaly through spatially anisotropic singularity mapping: A case study from silver-gold deposit in Pangxidong district, SE China. Journal of Geochemical Exploration. 1; 210:106453.
[17]. Farzamian, M., Mahdiyanfar, H., and Rouhani, A.K. (2022). Evidential belief functions modeling of geophysical and multi-element geochemical data for Pb-Zn mineral potential targeting. Journal of African Earth Sciences, p.104606.
[18]. Farhadi, S., Afzal, P., Boveiri Konari, M., Daneshvar Saein, L., and Sadeghi, B. (2022). Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran. Minerals. 12 (6): 689.
[19]. Borojerdnia A, Rozbahani M.M, Nazarpour A, Ghanavati N, and Payandeh K. (2020). Application of exploratory and Spatial Data Analysis (SDA), singularity matrix analysis, and fractal models to delineate background of potentially toxic elements: A case study of Ahvaz, SW Iran. Science of the Total Environment.10:140103.
[20]. Yang, Y., Yang, X., He, M., and Christakos, G. (2020). Beyond mere pollution source identification: Determination of land covers emitting soil heavy metals by combining PCA/APCS, GeoDetector and GIS analysis. Catena, 185, 104297.
[21]. Koohzadi, F., Afzal, P., Jahani, D., and Pourkermani, M. (2021). Geochemical exploration for Li in regional scale utilizing Staged Factor Analysis (SFA) and Spectrum-Area (SA) fractal model in north central Iran. Iranian Journal of Earth Sciences. 13 (4): 299-307.
[22]. Yousefi, M., Kamkar-Rouhani, A., and Carranza, E.J.M. (2012). Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping. Journal of Geochemical Exploration, 115, 24-35.
[23]. Shahi, H., Ghavami, R., Rouhani, A.K., Kahoo, A.R., and Haroni, H.A. (2015). Application of Fourier and wavelet approaches for identification of geochemical anomalies. Journal of African Earth Sciences, 106, 118-128.
[24]. Shahi, H., Ghavami, R., and Rouhani, A.K. (2016). Comparison of mineralization pattern of geochemical data in spatial and position-scale domain using new DWT-PCA approach. Journal of the Geological Society of India. 88 (2): 235-244.
[25]. Farzamian, M., Rouhani, A.K., Yarmohammadi, A., Shahi, H., Sabokbar, H.F., and Ziaiie, M. (2016). A weighted fuzzy aggregation GIS model in the integration of geophysical data with geochemical and geological data for Pb–Zn exploration in Takab area, NW Iran. Arabian Journal of Geosciences. 9 (2): p.104.
[26]. Abedi, M., Kashani, S.B.M., Norouzi, G.H., and Yousefi, M. (2017). A deposit scale mineral prospectivity analysis: A comparison of various knowledge-driven approaches for porphyry copper targeting in Seridune, Iran. Journal of African Earth Sciences, 128, 127-146.
[27]. Mahdiyanfar, H. (2020). Prediction of economic potential of deep blind mineralization by Fourier transform of a geochemical dataset. Periodico di Mineralogia, 90 (1).
[28]. Behera, S. and Panigrahi, M.K. (2021). Mineral prospectivity modelling using singularity mapping and multifractal analysis of stream sediment geochemical data from the auriferous Hutti-Maski schist belt, S. India. Ore Geology Reviews, 131, 104029.
[29]. Seyedrahimi-Niaraq, M. and Mahdiyanfar, H. (2021). Introducing a new approach of geochemical anomaly intensity index (GAII) for increasing the probability of exploration of shear zone gold mineralization. Geochemistry. 81 (4): 125830.
[30]. Alsop, E.B., Boyd, E.S., and Raymond, J. (2014). Merging metagenomics and geochemistry reveals environmental controls on biological diversity and evolution. BMC ecology. 14 (1): 1-12.
[31]. Reid, M.K. and Spencer, K.L. (2009). Use of principal components analysis (PCA) on estuarine sediment datasets: the effect of data pre-treatment. Environmental pollution. 157 (8-9): 2275-2281.
[32]. Zuo R. (2011). Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China). Journal of Geochemical Exploration. 1;111 (1-2): 13-22.
[33]. Heidari, S.M., Ghaderi, M., and Afzal, P. (2013). Delineating mineralized phases based on lithogeochemical data using multifractal model in Touzlar epithermal Au–Ag (Cu) deposit, NW Iran. Applied Geochemistry, 31, pp.119-132.
[34]. Yousefi, M. and Carranza, E.J.M. (2015). Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79: 69-81.
[35]. Geranian, H., Mokhtari, A.R., and Cohen, D.R. (2013). A comparison of fractal methods and probability plots in identifying and mapping soil metal contamination near an active mining area, Iran. Science of the total environment, 463, 845-854.
[36]. Seyedrahimi-Niaraq, M. and Mahdiyanfar, H. (2022). Improvement of geochemical prospectivity mapping using power spectrum–area fractal modeling of multi-element mineralization factor (SAF-MF). Geochemistry: Exploration, Environment, Analysis, geochem2022-015.
[37]. Mahdiyanfar H. (2019). Detection of Mo geochemical anomaly in depth using a new scenario based on spectrum–area fractal analysis. Journal of Mining and Environment. 1; 10 (3): 695-704.
[38]. Zuo, R. and Wang, J. (2016). Fractal/multifractal modeling of geochemical data: A review. Journal of Geochemical Exploration, 164, 33-41.
[39]. Cheng, Q. (1999). Spatial and scaling modelling for geochemical anomaly separation. Journal of Geochemical exploration. 65 (3): 175-194.
[40]. Mokhtari, A.R, Rodsari P.R, Cohen, D.R, Emami A, Bafghi, A.A., and Ghegeni, Z.K. (2015). Metal speciation in agricultural soils adjacent to the Irankuh Pb–Zn mining area, central Iran. Journal of African Earth Sciences. 1; 101:186-93.
[41]. Ahankoub M, Asahara Y, and Tsuboi M. (2020). Petrology and geochemistry of the Lattan Mountain magmatic rocks in the Sanandaj–Sirjan Zone, west of Iran. Arabian Journal of Geosciences. 13 (16): 1-3.
[42]. Ghazban, F., McNutt, R.H., and Schwarcz, H.P. (1994). Genesis of sediment-hosted Zn-Pb-Ba deposits in the Irankuh district, Esfahan area, west-central Iran. Economic Geology, 89(6), pp.1262-1278.
[43]. Fathianpour, N., Ghaedrahmati, R., and Hazery, M. (2009). Discrimination of Parts Bearing High Potential of Pb-Zn at Irankhoh Region in Isfahan in GIS Environment. Iranian Journal of Mining Engineering, 4 (8): pp.13-22.
[44]. Karimpour, M.H. and Sadeghi, M. (2018). Dehydration of hot oceanic slab at depth 30–50 km: KEY to formation of Irankuh-Emarat PbZn MVT belt, Central Iran. Journal of Geochemical Exploration, 194: 88–103.
[45]. Hosseini-Dinani H, Aftabi A, Esmaeili A, and Rabbani M. (2015). Composite soil-geochemical halos delineating carbonate-hosted zinc–lead–barium mineralization in the Irankuh district, Isfahan, west-central Iran. Journal of Geochemical Exploration. 1; 156:114-30.
[46]. Ghazifard, A. and Sharief, M. (2003). The study of the extent of heavy metal absorption by agricultural crops and investigating its environmental contamination around Irankuh Pb and Zn deposit. Isfahan Univ Res J. 17:153–66. (in Persian).
[47]. Kaiser, H.F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187–200.
[48]. Acal, C., Aguilera, A.M., and Escabias, M. (2020). New modeling approaches based on varimax rotation of functional principal components. Mathematics. 8 (11): p. 2085.
[49]. Zeng, M. (2021). Estimating Latent Factor Models in Matrices and Tensors via Spectral Methods and the Varimax Rotation. The University of Wisconsin-Madison.
[50]. Madani, N. and Sadeghi, B. (2019). Capturing Hidden Geochemical Anomalies in Scarce Data by Fractal Analysis and Stochastic Modeling. Natural Resources Research, 28: 833-847.
[51]. Dobrin, M.B. and Savit, C.H. (1988). Geophysical propecting: McGraw-Hill Book Co., New York, pp 867.
[52]. Bowen, H.J.M. (1979). The environmental chemistry of the elements. Academic Press, London, New York.
[53]. Zuo, R., Cheng, Q., and Xia, Q. (2009). Application of fractal models to characterization of vertical distribution of geochemical element concentration. Journal of Geochemical Exploration, 102: 37-43.
[54]. Pourgholam, M.M., Afzal, P., Yasrebi, A.B., Gholinejad, M., and Wetherelt, A. (2021). Detection of geochemical anomalies using a fractal-wavelet model in Ipack area, Central Iran. Journal of Geochemical Exploration.220:106675.
[55]. Mahdiyanfar, H. (2021). Identification of Buried Metal Ore Deposits using Geochemical Anomaly Filtering and Principal Factors of Power Spectrum. Journal of Mining and Environment. 12 (1): 205-218. | ||
|
آمار تعداد مشاهده مقاله: 258 تعداد دریافت فایل اصل مقاله: 467 |
||